Many locations are constantly or intermittently captured by video cameras. However, due to movements of the camera or the captured objects, the images are not clear enough, and stabilization may be required.
Abdullah, Tahir, and Samad in “Video stabilization based on point feature matching technique” published in Control and System Graduate Research Colloquium (ICSGRC), 2012 IEEE, vol., no., pp. 303, 307, 16-17 Jul. 2012 disclose an algorithm to stabilize jittery videos directly without the need to estimate camera motion. A stable output video will be attained without the effect of jittery that caused by shaking the handheld camera during video recording. Firstly, salient feature points from each frame of the input video is identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization and less unallied area after the process of stabilization.
Wei, Wei, and Batur in “Video stabilization and rolling shutter distortion reduction” published in IEEE International Conference on Image Processing (ICIP), 2010 17th, vol., no., pp. 3501, 3504, 26-29 Sep. 2010 and in, presents an algorithm that stabilizes video and reduces rolling shutter distortions using a six-parameter affine model that explicitly contains parameters for translation, rotation, scaling, and skew to describe transformations between frames. Rolling shutter distortions, including wobble, skew and vertical scaling distortions, together with both translational and rotational jitter are corrected by estimating the parameters of the model and performing compensating transformations based on those estimates. The results show the benefits of the proposed algorithm quantified by the Interframe Transformation Fidelity (ITF) metric.
US2011017601 discloses a method of processing a digital video sequence that includes estimating compensated motion parameters and compensated distortion parameters (compensated M/D parameters) of a compensated motion/distortion (M/D) affine transformation for a block of pixels in the digital video sequence, and applying the compensated M/D affine transformation to the block of pixels using the estimated compensated M/D parameters to generate an output block of pixels, wherein translational and rotational jitter in the block of pixels is stabilized in the output block of pixels and distortion due to skew, horizontal scaling, vertical scaling, and wobble in the block of pixels is reduced in the output block of pixels.
Battiato, Gallo, Puglisi and Scellato in “SIFT Features Tracking for Video Stabilization” published in the 14th International Conference on Image Analysis and Processing, 2007, pp. 825, 830, 10-14 Sep. 2007 discloses a video stabilization algorithm based on the extraction and tracking of scale invariant feature transform features through video frames. Implementation of SIFT operator is analyzed and adapted to be used in a feature-based motion estimation algorithm. SIFT features are extracted from video frames and then their trajectory is evaluated to estimate interframe motion. A modified version of iterative least squares method is adopted to avoid estimation errors and features are tracked as they appear in nearby frames to improve video stability. Intentional camera motion is eventually filtered with adaptive motion vector integration. Results confirm the effectiveness of the method.
Ken-Yi, Yung-Yu, Bing-Yu and Ming Ouhyoung in “Video stabilization using robust feature trajectories” published in Computer Vision, 2009 IEEE 12th International Conference on, vol., no., pp. 1397, 1404, Sep. 29, 2009-Oct. 2, 2009, disclose a method to directly stabilize a video without explicitly estimating camera motion, thus assuming neither motion models nor dominant motion. The method first extracts robust feature trajectories from the input video. Optimization is then performed to find a set of transformations to smooth out these trajectories and stabilize the video. In addition, the optimization also considers quality of the stabilized video and selects a video with not only smooth camera motion but also less unfilled area after stabilization.
Yasuyuki, Eyal, Xiaoou, and Heung-Yeung in “Full-Frame Video Stabilization” published in 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 50-57, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)—Volume 1, 2005, discloses that video stabilization is an important video enhancement technology which aims at removing annoying shaky motion from videos. Proposed is a practical and robust approach of video stabilization that produces full-frame stabilized videos with good visual quality. The completion method can produce full-frame videos by naturally filling in missing image parts by locally aligning image data of neighboring frames. To achieve this, motion inpainting is proposed to enforce spatial and temporal consistency of the completion in both static and dynamic image areas. In addition, image quality in the stabilized video is enhanced with a new practical deblurring algorithm. Instead of estimating point spread functions, the method transfers and interpolates sharper image pixels of neighbouring frames to increase the sharpness of the frame.
Veon, Mahoor, and Voyles in “Video stabilization using SIFT-ME features and fuzzy clustering” published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011, vol., no., pp. 2377, 2382, 25-30 Sep. 2011 proposes a digital video stabilization process using information that the scale-invariant feature transform (SIFT) provides for each frame. The process uses a fuzzy clustering scheme to separate the SIFT features representing global motion from those representing local motion. The process then calculates the global orientation change and translation between the current frame and the previous frame. Each frame's translation and orientation is added to an accumulated total, and a Kalman filter is applied to estimate the desired motion.
“Image Stabilization improving camera usability”, a white paper by Axis communications published on 2014 relates to a combination of gyroscopes and efficient algorithms for modeling camera motion.
U.S. Pat. No. 8,054,881 provides real-time image stabilization using computationally efficient corner detection and correspondence. The real-time image stabilization performs a scene learning process on a first frame of an input video to obtain reference features and a detection threshold value. The presence of jitter is determined in a current frame of the input video by comparing features of the current frame against the reference features using the detection threshold value. If the current frame is found to be unstable, corner points are obtained from the current frame. The obtained corner points are matched against reference corner points of the reference features. If the number of matched corner points is not less than a match point threshold value, the current frame is modeled using random sample consensus. The current frame is corrected to compensate for the jitter based on the results of the modeling.
U.S. Pat. No. 8,385,732 disclose image stabilization techniques used to reduce jitter associated with the motion of a camera. Image stabilization can compensate for pan and tilt (angular movement, equivalent to yaw and pitch) of a camera or other imaging device. Image stabilization can be used in still and video cameras, including those found in mobile devices such as cell phones and personal digital assistants (PDAs).